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38 results for “judgement”
Expert judgements for evaluating deduplication of OpenAIRE Research Graph
<p>Expert judgments used to evaluate the deduplication algorithm used for constructing the OpenAIRE Research Graph.</p> <p>Each expert assigned each group with one of the following predetermined classes (that also indicate whether contained entities are equivalent or not):</p> <ul> <li>AMBIGUOUS: At least one DOIs is invalid (no metadata are available) (N/A)</li> <li>DELETED-DUPLICATES: DOIs once pointing to the same research object, currently deleted. (TRUE)</li> <li>MULTI-PUBLISHED: Article published in more than one locations (full or abstract) (TRUE)</li> <li>VERSIONS: Multiple versions of the same research object (e.g. pre-prints, post-prints etc). (TRUE)</li> <li>ERRONEOUS: Unrelated set of objects. (FALSE)</li> <li>PAPER-EXTENSIONS: Extended version of a conference paper in a journal. (FALSE)</li> <li>PART-OF-A-GROUP: Multiple parts of the same research object (e.g. multi-part publication, photos of the same collection etc). (FALSE)</li> <li>SUPPLEMENTARY: A publication and its supplementary material (including errata). (FALSE)</li> </ul> <p>The following are provided:</p> <ul> <li>OpenAIRE identifier</li> <li>Judgement</li> <li>Count of numbers in group</li> <li>DOIs in group</li> </ul>
Crowdsourcing Document Similarity Judgements
<p>This is the data obtained from crowdsourcing tasks which ask workers to provide similarity metrics between pairs of documents. Each document, as well as each pair, has a unique ID. We provide crowd workers with the pairs through three different task variations:</p> <ul> <li>Variation 1: We showed workers 5 pairs of documents and, for each, asked them to rate their similarity in a 4-level Likert scale (None, Low, Medium, High), tell us a confidence level of how sure they were (from 0 to 4) and a written reason as to why they chose that similarity level. For quality reasons, two of the 5 pairs were golden-standards, which means we knew their ratings already and checked the workers' responses. They had to give the golden pair with the higher similarity a higher score than the other golden pair, otherwise, their answer would be rejected.</li> <li>Variation 2: We repeated variation 1 but with a slight alteration: instead of a Likert scale for the similarity score, we asked for a Magnitude Estimation, which is any number above 0. It could be 1, 0.0001, 1000, 42, as long as it was coherent, as in a more similar pair had a higher score than a less similar pair and vice-versa;</li> <li>Variation 3: We showed workers 5 rankings. Each ranking had a main document and 3 auxiliary documents to be compared against the main one. They also had to report a confidence score and give a short written reason, just like variation 1. The first ranking is a golden-standard, and we knew the values for the 3 pairs in it (the pairs were the main document paired with each of the 3 auxiliary documents), and they had to give the golden pair with the highest similarity a higher rank than the one with the lower similarity.</li> </ul> <p>The raw results from the tasks are recorded in the JSON file CrowdResults.json. For a description of its contents, please read the file CrowdResults_README.md.</p> <p>These raw annotations from the crowd were then parsed into the three CSVs you see, each corresponding to the aggregated results from one of the task variations.</p> <ul> <li><em>final_scores_likert.csv</em> is the resulting scores for each pair using the variation 1 tasks; <ul> <li><em>pair_id </em>is a unique identifier for each pair;</li> <li><em>similarity_alg </em>is the similarity assigned to the pair of documents from an automated similarity algorithm;</li> <li><em>relation </em>is the type of relationship shown by the pair, where smaller values indicate more similar pairs;</li> <li><em>similarity_crowd_simple_maj </em>stores the simple majority result from the crowd's annotations;</li> <li><em>similarity_crowd_simple_mean </em>stores the mean of the crowd's annotations;</li> <li><em>similarity_crowd_simple_median </em>stores the median of the crowd's annotations;</li> </ul> </li> <li><em>final_scores_magnitude.csv</em> is the resulting scores for each pair using the variation 2 tasks; <ul> <li><em>pair_id </em>is a unique identifier for each pair;</li> <li><em>similarity_alg </em>is the similarity assigned to the pair of documents from an automated similarity algorithm;</li> <li><em>relation </em>is the type of relationship shown by the pair, where smaller values indicate more similar pairs;</li> <li><em>scaled_similarity_worker</em> is the magnitude score scaled based on worker's behaviours</li> <li><em>scaled_similarity_worker_docset </em>is the magnitude score scaled based both on the worker's behaviour and on the pair</li> </ul> </li> <li><em>final_scores_ranking.csv</em> is the resulting scores for each pair using the variation 3 tasks; <ul> <li><em>pair_id </em>is a unique identifier for each pair;</li> <li><em>similarity_alg </em>is the similarity assigned to the pair of documents from an automated similarity algorithm;</li> <li><em>relation </em>is the type of relationship shown by the pair, where smaller values indicate more similar pairs;</li> <li><em>mean_similarity</em> is the mean ranking from that value</li> </ul> </li> </ul> <p>This dataset was built and used as part of the <a href="https://theybuyforyou.eu/">TheyBuyForYou </a>project.</p>
Chapter 3: Scale Separation Reliability: What Does it Mean in the Context of Comparative Judgement?
<p>This is the supplementary material for Chapter 3 of the dissertation "Beyond a Mere Rank Order: The Method, the Reliability and the Efficiency of Comparative Judgment" and the article "Scale separation reliability: What does it mean in the context of comparative judgment?" published in "Applied Psychological Measurement".</p>
Human and Machine Judgements for Russian Semantic Relatedness
<p>Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj , similarityij ). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.</p> <p>For more details see: </p> <ul> <li>The web page of the RUSSE evaluation campaign: http://russe.nlpub.ru/downloads</li> <li>The original publication "Panchenko A., Ustalov D., Arefyev N., Paperno D. Konstantinova N., Loukachevitch N. and Biemann C. undefinedHuman and Machine Judgements about Russian Semantic Relatedness. In Proceedings of the 5th Conference on Analysis of Images, Social Networks and Texts (AIST'2016). Communications in Computer and Information Science (CCIS). Springler-Verlag Berlin Heidelberg": https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/aist_2016_hmj.pdf</li> </ul>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19.</p>
A Meta-Analysis on the Reliability of Comparative Judgement Data
<p>This is the data and R analysis script with the article "A Meta-Analysis on the Reliability of Comparative Judgement"</p>
The accuracy and efficiency of a reference-based adaptive selection algorithm for comparative judgement
<p>Data and R code with article for Frontiers in Education special issue Validity, Reliability and Efficiency of Comparative Judgement to Assess Student Work. [Submitted]</p> <p>Abstract:</p> <p>Several studies have proven that comparative judgement (CJ) is a reliable and valid assessment method for a variety of competences, expert assessment and peer assessment, and CJ is emerging as a possible approach to help maintain standards over time. For consecutive pairs of student works (representations) assessors are asked to judge which representation is better. It has been shown that random construction of pairs leads to very inefficient assessments, requiring a lot of pairwise comparisons to reach reliable results. Some adaptive selection algorithms using information from previous comparisons were proposed to increase the efficiency of CJ. These adaptive algorithms appear however to artificially inflate the reliability of CJ results through increasing the spread of the results. The current article proposes a new adaptive selection algorithm using a previously calibrated reference set. Using a reference set should eliminate the reliability inflation. In a real assessment, using reference sets of different reliability, and in a simulation study it is proven that this adaptive selection algorithm is more efficient without reducing the accuracy of the results and without increasing the standard deviation of the assessment results. As a consequence, a reference-based adaptive selection algorithm produces high and correct reliability values in an efficient manner.</p>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review-Update: Systemic corticosteroids for the treatment of COVID-19.
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review-Update: Systemic corticosteroids for the treatment of COVID-19.</p>
Pama-Nyungan cognate judgements: 285 languages
<p>Tab-delimited export from Filemaker Pro of language data for 285 Pama-Nyungan languages, with cognate judgements. Based on NSF grants BCS-1423711 and BCS-0844550. This file contains the language name, subgroup, word, and multistate cognate judgement. For source information, see pamanyungan.net/chirila.</p> <p>Acknowledgement of the material is required for any derivative use.</p> <p>cf <a href="https://zenodo.org/record/3032846">https://zenodo.org/record/3032846</a> for some examples of work resulting from the dataset.</p>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis</p>
SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper
<p>This dataset contains:</p> <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Remdesivir for the treatment of COVID-19
<p>Updated Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Remdesivir for the treatment of COVID-19.</p>
The Role of Biological Motion in Face Perception: Social Judgements of Morphed Expressions Differ from Videos and Photographs.
<p>This study aimed to explore the impact of realistic and artifical facial dynamism on social percpetion. We compared ratings of emotion intensity and genuineness on video recordings, dynamic morphs, and static photographs of happy, sad, fearful, and angry expressions. The experiment was created using the online behavioural experiment builder Gorilla (AnwylIrvine et al., 2020). Each participant completed 144 trials, including 48 trials for each display type (video recording, morph, static), with 12 trials for each emotion (happy, disgust, fear, sadness). </p>
Data for "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023)
<p>Data used in the paper "Human judgement forecasting of COVID-19 in the UK" by Bosse et. al (2023). All code for the paper can be found at https://github.com/epiforecasts/uk-crowd-forecasting-challenge. </p>
Homology judgements of pre-Darwinian naturalists explained by general human shape matching abilities
<p>Dataset relative to the following publication:</p> <p>Stegmann, U., & Schmidt, F. (2023). Homology judgements of pre-evolutionary naturalists explained by general human shape matching abilities. <em>Scientific Reports, 13</em>, 12269.</p> <p>Each experiment folder contains the data relative to one experiment, together with a text file with comments.</p>
illegal gambling cases, searching from Chinese Judgement Online (裁判文书网)
Open the record for dataset details and reuse information.
Goelz_Finkel_Kehlbeck_Herschbach_Bauer_Scheib_Deussen_Randerath_AFFORDANCE JUDGEMENTS IN REAL AND VIRTUAL ENVIRONMENTS
<p><strong>Dataset belongs to manuscript:</strong></p> <p><strong>From virtual to real environments when judging action opportunities: Are diagnostics and trainings transferable?</strong></p> <p>Milena S. Gölz<sup>1, 2 </sup>*, Lisa Finkel<sup>1, 2 </sup>*, Rebecca Kehlbeck<sup>3</sup>, Anne Herschbach<sup>1, 2, 4</sup>, Isabel Bauer<sup>1, 2</sup>, Jean P. P. Scheib<sup>1, 2</sup>, Oliver Deussen<sup>3</sup> & Jennifer Randerath<sup>1, 2</sup></p> <p><sup>1</sup> Department of Psychology, University of Konstanz, Konstanz, Germany</p> <p><sup>2</sup> Lurija Institute for Rehabilitation Science and Health Research, Allensbach, Germany</p> <p><sup>3</sup> Department of Computer and Information Science, Centre for the Advanced Study of Collective Behavior, University of Konstanz, Konstanz, Germany</p> <p><sup>4</sup> Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital Tuebingen, Tuebingen, Germany</p> <p> </p> <p>* Shared first authorship</p>
Carpal Tunnel Syndrome, Right/Left Judgement
ClinicalTrials.gov study NCT04967144. IPD Sharing: NO. Countries: 1. Publications: 5.
Sex differences in moral judgements across 67 countries
Most of the empirical research on sex differences and cultural variations in morality has relied on within-culture analyses or small-scale cross-cultural data. To further broaden the scientific understanding of sex differences in morality, the current research relies on two international samples to provide the first large-scale examination of sex differences in moral judgements nested within cultures. Using a sample from 67 countries (Study 1; n = 336,691), we found culturally-variable sex differences in moral judgements, as conceptualized by Moral Foundations Theory. Women consistently scored higher than men on Care, Fairness, and Purity. In contrast, sex differences in Loyalty and Authority were negligible and highly variable across cultures. Country-level sex differences in moral judgements were also examined in relation to cultural, socio-economic, and gender-equality indicators revealing that sex differences in moral judgements are larger in individualist, Western, and gender-equal societies. In Study 2 (19 countries; n = 11,969), these results were largely replicated using Bayesian multi-level modeling in a distinct sample. The findings were robust even when incorporating cultural non-independence of countries into the models. Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in their moral judgements than did men. Sex differences in moral judgements were larger in individualist and gender-equal societies with more flexible social norms. We discuss the implications of these findings for the ongoing debate about the origin of sex differences and cultural variations in moral judgements as well as theoretical and pragmatic implications for moral and evolutionary psychology.
Data from: Data-driven mathematical model of East-Asian facial attractiveness: the relative contributions of shape and reflectance to attractiveness judgements
Facial attractiveness is judged through a combination of multiple cues including morphology (facial shape) and skin properties (facial reflectance). While several studies have examined the way in which people in Western cultures judge facial attractiveness, there have been fewer investigations into non-Western attitudes. This is because stimuli that quantitatively vary the attractiveness of non-Western faces are rare. In the present study, we built a model of the attractiveness of East-Asian faces, judged by East-Asian observers. Therefore, 400 computer-generated East-Asian faces were created and attractiveness rating scores were collected from Japanese observers. Data-driven mathematical calculations were used to identify quantitative links between facial attractiveness and shape and reflectance properties, with no prior hypothesis. Results indicate that faces with larger eyes, smaller noses, and brighter skin are judged as more attractive, regardless of the sex of the faces, possibly reflecting a general preference for femininity. Shape is shown to be a strong determinant of attractiveness for both male and female faces, while reflectance properties are less important in judging male facial attractiveness. Our model provides a tool to effectively produce East-Asian face stimuli that quantitatively varies attractiveness and can be used to elucidate visual processes related to attractiveness judgment.
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